class_docstr: The ResNet_vd implementation based on PaddlePaddle.
  
  The original article refers to Jingdong
  Tong He, et, al. "Bag of Tricks for Image Classification with Convolutional Neural Networks"
  (https://arxiv.org/pdf/1812.01187.pdf).
  
  Args:
      layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
      output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
      multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
      pretrained (str, optional): The path of pretrained model.
code: def init_weight(self):
      ppmatting.utils.load_pretrained_model(self, self.pretrained)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  import ppmatting
label_desc: Use image node_id 3 for calling the ResNet_vd obj's underlying member method code with example usage: obj.init_weight() without return types